Vehicle trajectory prediction based on motion model and maneuver recognition

Adam Houenou, Philippe Bonnifait, Veronique Cherfaoui, Wen Yao
2013 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems  
Predicting other traffic participants trajectories is a crucial task for an autonomous vehicle, in order to avoid collisions on its planned trajectory. It is also necessary for many Advanced Driver Assistance Systems, where the egovehicle's trajectory has to be predicted too. Even if trajectory prediction is not a deterministic task, it is possible to point out the most likely trajectory. This paper presents a new trajectory prediction method which combines a trajectory prediction based on
more » ... ant Yaw Rate and Acceleration motion model and a trajectory prediction based on maneuver recognition. It takes benefit on the accuracy of both predictions respectively a short-term and long-term. The defined Maneuver Recognition Module selects the current maneuver from a predefined set by comparing the center lines of the road's lanes to a local curvilinear model of the path of the vehicle. The overall approach was tested on prerecorded human real driving data and results show that the Maneuver Recognition Module has a high success rate and that the final trajectory prediction has a better accuracy.
doi:10.1109/iros.2013.6696982 dblp:conf/iros/HouenouBCY13 fatcat:ckng7acn6zea5k4ukoiswo2xcm